UPDATE: slides and materials can be found at http://pietrobattiston.it/python:pycon#europython_rimini_july_2017
The pandas library represents a very efficient and convenient tool for data manipulation, but sometimes hides unexpected pitfalls which can arise in various and sometimes unintelligible ways.
By briefly referring to some aspects of the implementation, I will review specific situations in which a change of approach can make code based on pandas more robust, or more performant.
Some examples:
inefficient indexing
multiple dtypes and efficiency
implicit type casting
HDF5 storage overhead
GroupBy.apply()... when you don't actually need it
Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/
The pandas library represents a very efficient and convenient tool for data manipulation, but sometimes hides unexpected pitfalls which can arise in various and sometimes unintelligible ways.
By briefly referring to some aspects of the implementation, I will review specific situations in which a change of approach can make code based on pandas more robust, or more performant.
Some examples:
inefficient indexing
multiple dtypes and efficiency
implicit type casting
HDF5 storage overhead
GroupBy.apply()... when you don't actually need it
Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/
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